1. Field of the Invention
The present invention relates to a fingerprint registering apparatus, a fingerprint identifying apparatus, and a fingerprint identifying method, in particular, to those suitable for identifying a person.
2. Description of the Related Art
As computers have been widely used in our society, system security has gained public attention. For example, as a means for identifying a person who accesses a computer room or who uses a terminal unit, an ID card or an unique password is used. However, such means have a problem from a viewpoint of security.
Instead of using an ID card or a password, a person identifying technology that uses information intrinsic to a living body is desired. When such information is used for identifying a person, since other people do not have the same information, the information of the living body assures the identity of the person.
As types of information of the living body used for identifying a person, there are fingerprint, voice-print, iris, distribution chart of retinal veins, signature, and so forth. These types of information of a living body are converted into electronic information such as an image by various sensors such as a CCD camera. Thereafter, information of a living body obtained by such a sensor is processed and then information that becomes a key for identifying a person is extracted. Therefore, information of a living body that has been registered is identified with key information of a living body that has been input so as to identify a person.
Next, as an example of information of a living body, a fingerprint will be described.
A fingerprint has two major features that are "no fingerprints are the same" and "no change in a life time". Thus, it is considered that a fingerprint is the most effective means for identifying a person. Consequently, many simple person-identifying systems using fingerprints have been intensively studied and developed.
A fingertip of a human being has fine concave portions and fine convex portions. A string of a convex portion is referred to as a ridge. Ridges form a pattern intrinsic to a person. When a ridge is traced, a ridge bifurcation and a ridge ending take place. At a ridge bifurcation, the ridge bifurcates out into two ridges. At a ridge ending, the ridge breaks. Since the distribution of ridge bifurcations and ridge endings varies person by person, these points are referred to as feature points. The feature points are used as a prominent means for identifying a person. When fingerprints are identified, it is determined whether the positions, types, and directions of these feature points match.
FIG. 1 is a flowchart showing a conventional fingerprint registering process and a conventional fingerprint identifying process.
In the fingerprint registering process, a fingerprint image to be registered is sampled by a fingerprint sensor (at step S191). The sampled fingerprint image is binarized (at step S192).
Next, thin lines are generated with the binarized fingerprint image (at step S193). Thus, a thin line image of a fingerprint in which the width of each ridge is expressed by one pixel is obtained.
Thereafter, the positions of the feature points of the fingerprint are located and extracted on the thin line image of a fingerprint (at step S194). Since the extracted feature points generally contain incorrect feature points, these incorrect feature points are removed (at step S195). When these incorrect feature points are removed, if two ridge endings are opposite each other, separated by a short distance, these ridge endings are treated as one ridge that has broken in the image sampling process. In this case, the ridge between the two ridge endings is restored. Thus, the two ridge endings are removed. On the other hand, when two parallel ridges adhere in the middle and thereby a ridge bifurcation takes place, the ridge bifurcations are treated as two parallel ridges that have adhered in the image sampling process. In this case, the ridges that have adhered are separated and thereby the ridge bifurcation is removed.
Next, fingerprint information of each feature point extracted from the fingerprint image is collected (at step S196). The fingerprint information is stored as registered fingerprint data in a fingerprint data registering unit 110.
In the fingerprint identifying process, a fingerprint image to be identified is sampled by a fingerprint sensor (at step S197). The sampled fingerprint image is binarized (at step S198).
Next, thin lines are generated with the binarized fingerprint image (at step S199). Thus, a thin line image of a fingerprint, of which the width of each ridge is expressed by one pixel is obtained.
Next, the positions of features points of the fingerprint are located and extracted on the thin line image of a fingerprint (at step S200). Since the extracted feature points generally contain incorrect feature points, the incorrect feature points are removed (at step 5201).
Next, fingerprint information of each feature point extracted from the fingerprint image is collected (at step S202). The registered fingerprint data is read from a fingerprint data registering unit 110 so as to align the positions of the input fingerprint image and the registered fingerprint image (at step S203).
Next, the fingerprint information of the input fingerprint image and the fingerprint registered data that has been read from the fingerprint data registering unit 110 are compared so as to identify the input fingerprint image and the registered fingerprint image. In other words, the number of feature points that match in the input fingerprint image and the registered fingerprint image is counted (at step S204). When the number of feature points that match in the input fingerprint image and the registered fingerprint image exceeds a predetermined value (at step S205), it is determined that the fingerprint of the registered fingerprint image is the same as the fingerprint of the input fingerprint image (at step S206). On the other hand, when the number of feature points that match in the input fingerprint image and the registered fingerprint image is smaller than the predetermined value (at step S205), it is determined that the fingerprint of the registered fingerprint image is different from the fingerprint of the input fingerprint image (at step S207).
As feature point information of feature points, the positions (coordinates), types, and directions of individual feature points are generally used. By determining whether the positions, types, and directions of feature points match in the input fingerprint image and the registered fingerprint image, the fingerprints are identified.
However, when the positions, types, and direction of feature points of the input fingerprint image and the registered fingerprint image are compared, a high identification ratio cannot be obtained. Since the skin of a finger partially expands, shrinks, or rotates, whenever a fingerprint image is sampled, the fingerprint distorts. Thus, the positions and directions of feature points delicately vary. In addition, due to an improper pressure of a finger to the fingerprint sensor, dirty thereof, chaps of a finger, and so forth, whenever a fingerprint image is sampled, a ridge ending and/or a ridge bifurcation is unstably detected. Thus, the identification ratio deteriorates.